


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase					:       Tuple			    =     logging.get_logger(__name__)

UpperCamelCase					:       List[Any]			    =     {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		'umt5'
  _lowercase									=		['past_key_values']

  def __init__( self			:  Union[str, Any]				,				lowerCamelCase__			:  List[Any]=250_112				,				lowerCamelCase__			:  str=512				,				lowerCamelCase__			:  Optional[Any]=64				,				lowerCamelCase__			:  List[Any]=1_024				,				lowerCamelCase__			:  int=8				,				lowerCamelCase__			:  List[str]=None				,				lowerCamelCase__			:  Optional[Any]=6				,				lowerCamelCase__			:  int=32				,				lowerCamelCase__			:  Union[str, Any]=128				,				lowerCamelCase__			:  Tuple=0.1				,				lowerCamelCase__			:  Optional[Any]=1E-6				,				lowerCamelCase__			:  Union[str, Any]=1.0				,				lowerCamelCase__			:  Union[str, Any]="gated-gelu"				,				lowerCamelCase__			:  Any=True				,				lowerCamelCase__			:  List[str]=True				,				lowerCamelCase__			:  Any="T5Tokenizer"				,				lowerCamelCase__			:  Union[str, Any]=True				,				lowerCamelCase__			:  Optional[Any]=0				,				lowerCamelCase__			:  List[Any]=1				,				lowerCamelCase__			:  List[str]=0				,				**lowerCamelCase__			:  Any				,				):
        super().__init__(
            is_encoder_decoder=lowerCamelCase__				,				tokenizer_class=lowerCamelCase__				,				tie_word_embeddings=lowerCamelCase__				,				pad_token_id=lowerCamelCase__				,				eos_token_id=lowerCamelCase__				,				decoder_start_token_id=lowerCamelCase__				,				**lowerCamelCase__				,				)
        a__	:  Dict     =   vocab_size
        a__	:  List[Any]     =   d_model
        a__	:  Optional[Any]     =   d_kv
        a__	:  Optional[int]     =   d_ff
        a__	:  List[Any]     =   num_layers
        a__	:  int     =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        a__	:  str     =   num_heads
        a__	:  Optional[int]     =   relative_attention_num_buckets
        a__	:  Any     =   relative_attention_max_distance
        a__	:  int     =   dropout_rate
        a__	:  Union[str, Any]     =   layer_norm_epsilon
        a__	:  Optional[int]     =   initializer_factor
        a__	:  Optional[int]     =   feed_forward_proj
        a__	:  Tuple     =   use_cache

        a__	:  List[Any]     =   self.feed_forward_proj.split("-"       )
        a__	:  Optional[int]     =   act_info[-1]
        a__	:  Union[str, Any]     =   act_info[0] == "gated"

        if len(lowerCamelCase__       ) > 1 and act_info[0] != "gated" or len(lowerCamelCase__       ) > 2:
              raise ValueError(
                  f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                  "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                  "'gated-gelu' or 'relu'"       )

        if feed_forward_proj == "gated-gelu":
              a__	:  Optional[int]     =   "gelu_new"

  @property
  def    _UpperCamelCase( self			:  int       ):
        return self.d_model

  @property
  def    _UpperCamelCase( self			:  int       ):
        return self.num_heads

  @property
  def    _UpperCamelCase( self			:  Dict       ):
        return self.num_layers




class A__     ( A__  ):



  """simple docstring"""



  @property
  # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  Dict     =   {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
              a__	:  Tuple     =   "past_encoder_sequence + sequence"
              a__	:  Dict     =   {0: "batch"}
              a__	:  Union[str, Any]     =   {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
              a__	:  Tuple     =   {0: "batch", 1: "decoder_sequence"}
              a__	:  List[str]     =   {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
              self.fill_with_past_key_values_(lowerCamelCase__				,				direction="inputs"       )

        return common_inputs

  @property
  # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  def    _UpperCamelCase( self			:  int       ):
        return 13

  @property
  def    _UpperCamelCase( self			:  List[str]       ):
        return 5E-4



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	int       			=  {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""trocr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }
  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=5_0265     ,				UpperCamelCase__     :					int=1024     ,				UpperCamelCase__     :					int=12     ,				UpperCamelCase__     :					List[Any]=16     ,				UpperCamelCase__     :					Union[str, Any]=4096     ,				UpperCamelCase__     :					List[Any]="gelu"     ,				UpperCamelCase__     :					Optional[int]=512     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					int=0.0     ,				UpperCamelCase__     :					str=0.0     ,				UpperCamelCase__     :					Optional[int]=2     ,				UpperCamelCase__     :					List[str]=0.02     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					str=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					List[str]=2     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			activation_function
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			init_std
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			use_cache
    __magic_name__								=			scale_embedding
    __magic_name__								=			use_learned_position_embeddings
    __magic_name__								=			layernorm_embedding

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

